Gastos Casa
03 July, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 13/5/2024 | Comida | 28480 | Andrés | piwen |
| 13/5/2024 | Enceres | 40000 | Andrés | mantencion toyotomi |
| 17/5/2024 | Comida | 59132 | Tami | Supermercado |
| 18/5/2024 | Gas | 94000 | Andrés | 2 lks mas |
| 24/5/2024 | Comida | 22384 | Tami | Barritas wild soul |
| 26/5/2024 | Comida | 53958 | Tami | Supermercado |
| 28/5/2024 | cartero | 4000 | Andrés | NA |
| 29/5/2024 | Electricidad | 35206 | Andrés | NA |
| 1/6/2024 | Comida | 68402 | Tami | NA |
| 1/6/2024 | Comida | 11560 | Andrés | lider gastos |
| 1/6/2024 | Comida | 16500 | Andrés | teteria |
| 2/6/2024 | Parafina | 39138 | Tami | NA |
| 4/6/2024 | Comida | 6780 | Andrés | avena |
| 9/6/2024 | Comida | 67000 | Tami | Supermercado |
| 15/6/2024 | Gas | 17550 | Andrés | NA |
| 15/6/2024 | Comida | 6530 | Tami | Cus cus y orégano |
| 17/6/2024 | VTR | 21990 | Andrés | NA |
| 19/6/2024 | Diosi | 18180 | Tami | Pelet y pastita Diosi |
| 22/6/2024 | VTR | 22000 | Andrés | entel |
| 23/6/2024 | Comida | 70795 | Tami | Supermercado |
| 25/6/2024 | Enceres | 11790 | Tami | Confort 40 rollos |
| 28/6/2024 | Diosi | 16002 | Andrés | pipeta |
| 29/6/2024 | Comida | 65070 | Tami | Supermercado |
| 29/6/2024 | Comida | 6400 | Andrés | lider |
| 29/6/2024 | Electricidad | 55385 | Andrés | pac enel |
| 29/6/2024 | Enceres | 42000 | Andrés | ida al sodimac |
| 1/7/2024 | Comida | 40000 | Andrés | piwen |
| 2/7/2024 | Comida | 2426 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.0505e+08 2 7.2537 8e-04 ***
## lag_depvar 1.2189e+11 1 2196.4415 <2e-16 ***
## Residuals 4.0288e+10 726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 563.2538 13894.42 0.0297534
## 2-0 29623.700 23597.0562 35650.34 0.0000000
## 2-1 22394.862 18873.9840 25915.74 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 252 44028.00 2 38081.43
## 253 47139.71 2 44028.00
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## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
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## 259 72207.14 2 70510.86
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## 263 50113.14 2 62390.71
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## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
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## 290 74644.71 2 70168.57
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## 293 34704.86 2 41560.57
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## 295 50231.00 2 46520.00
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## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
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## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
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## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
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## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
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## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
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## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
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## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
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## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 574 51857.96 16219.205
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1980.60957 4023.97006 -521.75573 2452.21620 -2937.36752 530.47320
## 8 9 10 11 12 13
## -5638.60595 -1207.16519 -3987.50873 -459.70240 -4975.48343 -1670.22867
## 14 15 16 17 18 19
## -960.16289 322.18771 -3285.12257 -433.00280 -2177.24622 6552.20976
## 20 21 22 23 24 25
## -1527.00190 -1213.14111 1466.76793 -1180.98707 235.09629 1700.46338
## 26 27 28 29 30 31
## -7082.41863 920.78239 8178.96936 464.97062 33.30885 -2355.71232
## 32 33 34 35 36 37
## 1602.96703 4610.07375 1194.00364 2461.26967 -1787.31321 4669.58694
## 38 39 40 41 42 43
## 4340.12404 -2214.49472 -2942.84714 -1096.05835 -10736.26152 7222.29646
## 44 45 46 47 48 49
## 2547.70976 1375.90674 8122.52716 755.87525 6594.59485 6815.93877
## 50 51 52 53 54 55
## -5748.33872 -4717.38154 -5023.28243 -7930.25441 6075.59803 -4082.68112
## 56 57 58 59 60 61
## -4926.61689 3795.12461 860.87183 -49.46299 127.12789 -5008.39497
## 62 63 64 65 66 67
## 18083.18270 3724.13360 -3548.48060 5986.63790 7437.61540 14770.21336
## 68 69 70 71 72 73
## 1906.37501 -13014.64316 -1220.93613 4709.67151 -4811.00916 -4358.85079
## 74 75 76 77 78 79
## -10486.50822 2406.89269 -5435.16036 997.31909 -6916.76785 457.49529
## 80 81 82 83 84 85
## -2428.61764 -2773.80082 -4019.21238 -639.55755 2222.30667 3697.79093
## 86 87 88 89 90 91
## 445.53276 -508.52296 172.70077 4282.62182 -1150.99164 1153.90081
## 92 93 94 95 96 97
## -2053.85560 -1048.45532 167.30069 267.27918 -7488.46923 2338.51481
## 98 99 100 101 102 103
## -8632.74516 -3023.64615 -4131.00652 -1842.92592 -1365.06044 3082.51141
## 104 105 106 107 108 109
## -2406.54028 2522.53032 -1203.30101 923.90564 2553.19762 -3166.51606
## 110 111 112 113 114 115
## -4754.22778 -908.44647 1847.43302 11657.52826 -1196.17551 2701.17939
## 116 117 118 119 120 121
## 4309.43560 3572.06144 -1015.67436 -4649.59090 -3696.65628 2319.45999
## 122 123 124 125 126 127
## -1717.12032 1342.90824 8869.71357 916.29928 196.93373 -2461.90682
## 128 129 130 131 132 133
## 2690.60402 7101.58787 1102.50817 -8413.59811 1768.14443 4164.03517
## 134 135 136 137 138 139
## -3111.23017 -1394.29664 -840.80607 -3873.79347 1163.11291 -504.67299
## 140 141 142 143 144 145
## -2924.55938 1689.63006 -1894.25948 -7852.94376 1967.22606 -3528.96664
## 146 147 148 149 150 151
## 2036.42768 -300.74378 983.79290 -386.52290 1326.30559 1173.25783
## 152 153 154 155 156 157
## 3353.04216 -4842.33242 -1189.40020 -3256.32817 5917.65102 9752.30464
## 158 159 160 161 162 163
## -3506.87388 -4865.76952 3496.84306 125.77128 2637.80781 -5943.35917
## 164 165 166 167 168 169
## -6814.50070 4054.36347 17330.43945 3679.97598 -331.37402 -2390.80367
## 170 171 172 173 174 175
## -1072.18533 3610.55548 -189.36265 -8044.03879 2839.51392 4320.98330
## 176 177 178 179 180 181
## 647.20914 8771.85497 -9177.03098 -3468.20001 -10764.51959 -11325.67457
## 182 183 184 185 186 187
## 1088.98780 9172.14860 -1480.00261 5874.28206 6541.56858 13182.26045
## 188 189 190 191 192 193
## 8525.71796 -3936.28596 2544.24958 10445.80486 -1521.98071 -2355.25299
## 194 195 196 197 198 199
## -10223.74157 -6380.26215 1176.28023 -5280.43516 -9871.99370 5256.38239
## 200 201 202 203 204 205
## -3150.42192 -1805.23931 -898.28798 6404.16039 9835.64820 587.57715
## 206 207 208 209 210 211
## 2929.68568 3112.94963 5809.14856 12882.32739 -5577.81894 -11238.94571
## 212 213 214 215 216 217
## -5685.99226 -10641.03072 -5185.93187 1398.33424 -13116.64917 16220.02130
## 218 219 220 221 222 223
## 7751.19986 1516.49863 26681.35072 12662.49889 7515.57354 14217.02955
## 224 225 226 227 228 229
## -3677.38938 -1564.76914 3914.23911 494.38461 2859.99936 9114.92982
## 230 231 232 233 234 235
## 5974.35450 -1749.77395 -1705.88402 9517.93917 -11378.83064 -7240.51351
## 236 237 238 239 240 241
## -8549.68029 -10161.75954 2963.74693 1266.85693 -8367.23982 -9101.08760
## 242 243 244 245 246 247
## 8943.58045 -7849.21666 2367.00090 -10395.56062 -4197.58215 1271.35353
## 248 249 250 251 252 253
## 875.43919 -12425.46307 3475.26519 1932.31256 4105.33313 2060.36089
## 254 255 256 257 258 259
## -1218.88058 11076.53541 20880.54672 3299.91898 -4173.91738 4162.65833
## 260 261 262 263 264 265
## -1634.45209 3772.47646 -4808.77531 -10889.79987 -4790.63344 -607.40303
## 266 267 268 269 270 271
## -5271.99028 8671.02300 -4334.84666 4111.60416 -2160.72649 4364.89137
## 272 273 274 275 276 277
## 666.20642 7260.64549 -1420.80999 12000.46657 -4556.00814 1715.09012
## 278 279 280 281 282 283
## -383.25939 7829.56745 -5048.06326 -2757.93882 -11306.70305 -2770.88699
## 284 285 286 287 288 289
## 18546.76002 7771.27532 2750.43380 -612.16388 906.59933 6392.54390
## 290 291 292 293 294 295
## 6897.04956 -18738.24016 -11204.50697 -8234.81698 9525.39354 2990.65972
## 296 297 298 299 300 301
## -1241.69273 27336.00796 10122.87504 4985.32770 9602.56229 2961.76344
## 302 303 304 305 306 307
## -939.00876 7962.89105 -24212.53382 -3575.27983 -230.36497 -7021.05676
## 308 309 310 311 312 313
## -4051.14762 2843.82117 -9258.84422 -3328.46032 -8285.48637 1445.42622
## 314 315 316 317 318 319
## -3248.54351 1950.11837 -4160.57362 27358.66320 -710.10919 3291.48631
## 320 321 322 323 324 325
## 10835.50378 5628.80297 32428.66636 5276.43129 -20781.11071 1835.59753
## 326 327 328 329 330 331
## 1148.51834 -6433.97385 -1738.90129 -33283.33941 769.57483 -2387.85335
## 332 333 334 335 336 337
## -168.44930 -3225.97568 4030.08008 -463.44076 -6971.46013 -3153.09495
## 338 339 340 341 342 343
## -2228.26163 -7712.57266 3801.50380 -1394.16968 -1756.86598 -1010.85290
## 344 345 346 347 348 349
## 164.17611 477.02390 -1615.93003 -9445.97751 -13239.64810 2241.84044
## 350 351 352 353 354 355
## -4367.60865 -3706.22897 -6027.85005 1694.42726 1347.45678 2730.32349
## 356 357 358 359 360 361
## -3773.80874 -533.32780 663.59409 7006.47697 295.55182 -20.15571
## 362 363 364 365 366 367
## 2599.36626 -2726.66166 -864.32680 -8732.35932 -4640.28603 -6233.96705
## 368 369 370 371 372 373
## -4981.71797 -7289.48646 4966.78363 350.11076 7106.86050 -7622.08033
## 374 375 376 377 378 379
## -2270.59942 -3396.23345 -2479.07688 -12469.55069 1859.80334 -10658.87303
## 380 381 382 383 384 385
## 5647.86146 9317.39507 3141.34007 -2374.64641 1619.37643 6762.80096
## 386 387 388 389 390 391
## 11446.62864 -5741.02400 -5334.44809 -153.60549 8563.56979 1836.98695
## 392 393 394 395 396 397
## 11240.99985 -9835.84912 2772.71006 713.68397 559.50871 -660.26074
## 398 399 400 401 402 403
## -575.91324 -14504.80009 8468.57733 -1198.74767 -1389.88635 6964.34705
## 404 405 406 407 408 409
## -7927.41533 -1312.83520 -2544.58138 -5832.02732 -2879.34758 -3935.00757
## 410 411 412 413 414 415
## -8773.84503 6101.53682 1636.10801 -7367.91046 -7701.89964 14198.78595
## 416 417 418 419 420 421
## 3836.89824 4518.25563 -8003.54039 -4736.54658 -2602.34362 2817.75216
## 422 423 424 425 426 427
## -14000.17027 -2813.60611 -9117.21208 2981.34165 6965.48462 6588.50816
## 428 429 430 431 432 433
## -3956.31787 -4101.02666 -4711.05293 -1786.61561 -5707.76074 -6631.92294
## 434 435 436 437 438 439
## -5964.84206 -1414.72545 -862.20448 -4982.41232 2569.16531 4842.41071
## 440 441 442 443 444 445
## -5035.81994 -2148.37833 1585.20050 -3819.34983 2846.34369 -6555.42990
## 446 447 448 449 450 451
## -12103.05469 -4531.42387 9623.94083 -2014.95156 4770.62231 -5835.18437
## 452 453 454 455 456 457
## -1102.76663 405.26059 3053.10835 -12229.91853 3373.61725 -6679.84194
## 458 459 460 461 462 463
## 6528.63173 3044.32062 2549.87452 -3795.63457 2130.76305 37.71032
## 464 465 466 467 468 469
## 1837.64189 -471.84036 3396.90610 -2584.02720 5850.84468 -6879.62602
## 470 471 472 473 474 475
## -2923.54466 -2170.40697 -4631.25946 3019.85589 7835.02620 -5954.07166
## 476 477 478 479 480 481
## 1528.32813 -6128.24318 -2810.67083 2041.69121 -12889.23542 -9751.09182
## 482 483 484 485 486 487
## -1219.43177 11.96794 -959.39450 -1332.82395 -9571.66587 11085.90093
## 488 489 490 491 492 493
## 6271.29474 7478.78318 -5356.15013 5426.80010 9367.87407 6152.96382
## 494 495 496 497 498 499
## -13364.45960 -10507.08797 -3414.42400 -1083.75332 -498.89182 -7595.87123
## 500 501 502 503 504 505
## 623.61795 4311.26506 5552.57402 724.68314 146.74113 -7173.01778
## 506 507 508 509 510 511
## 613.27572 -4999.39116 1868.62968 -1249.50946 -8111.77409 -578.37429
## 512 513 514 515 516 517
## -2645.52087 -560.95063 1363.74601 -9453.66000 -7749.98451 24285.03868
## 518 519 520 521 522 523
## 9911.34819 5991.85829 -5214.09292 2892.11029 17115.08179 11615.09644
## 524 525 526 527 528 529
## -23987.57831 -4992.71504 -3684.36090 4610.25698 -302.00755 -11049.64708
## 530 531 532 533 534 535
## 4409.92961 13947.99788 -4892.81659 4436.01480 5628.23919 -1704.65720
## 536 537 538 539 540 541
## -4467.41705 -7018.62214 -2067.19067 8353.42144 188.35555 -8081.02454
## 542 543 544 545 546 547
## 1850.35506 -555.51591 410.84104 -10982.79660 -11046.15462 2029.08968
## 548 549 550 551 552 553
## 7010.59894 -1279.32267 875.08949 -7675.79761 8588.61377 963.85217
## 554 555 556 557 558 559
## -11883.61312 9185.05354 8714.72124 182.20294 4931.20674 -3485.86502
## 560 561 562 563 564 565
## 14179.21053 21609.09535 -6301.50533 -9556.84682 6855.26270 321.10836
## 566 567 568 569 570 571
## 3541.47415 -7286.94252 -17247.24047 6670.27469 6472.72447 1964.65734
## 572 573 574 575 576 577
## 3166.50957 1846.82867 -2085.06774 14787.12034 -9535.22713 -6171.82209
## 578 579 580 581 582 583
## 8761.41157 2937.01518 -6461.71756 7567.59257 -3717.52077 -2709.21139
## 584 585 586 587 588 589
## 15759.24287 -14392.72382 8479.83854 150.17127 -6135.41084 -698.86247
## 590 591 592 593 594 595
## 306.90908 -10595.25940 1822.85828 -7104.99989 3089.76111 8906.80190
## 596 597 598 599 600 601
## -7426.66345 5900.57674 2806.34572 6935.62409 -3090.05946 6234.32429
## 602 603 604 605 606 607
## -8196.13340 2325.80747 1348.24869 3219.86831 1585.70107 490.13241
## 608 609 610 611 612 613
## -5719.41266 8144.50214 -1086.28807 -2483.55195 -3372.66134 -8158.52647
## 614 615 616 617 618 619
## 12003.57339 5027.51672 -9208.22659 11697.85985 6162.56518 -5444.36849
## 620 621 622 623 624 625
## 26463.27560 -12632.57155 -6645.05226 3262.42197 -4045.15997 -10496.00804
## 626 627 628 629 630 631
## 11355.27153 -21532.01803 -2390.43110 8701.60177 11203.24435 -1441.22056
## 632 633 634 635 636 637
## 33387.30544 -6365.96165 5895.35617 5584.99366 -2077.33011 -5176.79809
## 638 639 640 641 642 643
## -1802.16748 -12306.80082 -2168.07414 -1817.18217 -2454.29628 -2796.79374
## 644 645 646 647 648 649
## 1871.01262 4499.47120 17053.57425 18704.26779 1092.56180 4982.72303
## 650 651 652 653 654 655
## 10806.85976 20368.93591 1022.69879 -27807.14018 -1211.87787 -2168.77244
## 656 657 658 659 660 661
## 1981.69561 -3069.87137 -10511.75530 1734.73679 4310.57333 -900.31299
## 662 663 664 665 666 667
## 13136.71407 1519.59929 1972.43162 -11530.40177 1485.90310 1302.19644
## 668 669 670 671 672 673
## -5044.65669 -7309.13451 2141.17023 -3620.21545 2754.81485 -3279.28819
## 674 675 676 677 678 679
## -9247.18445 -8252.40298 -2952.39357 196.94121 2884.93920 775.17628
## 680 681 682 683 684 685
## -3752.97296 -1745.10962 -1254.80891 -8176.71494 4684.87621 -2173.42558
## 686 687 688 689 690 691
## -1331.98381 655.23434 10931.79957 9983.81470 10802.63998 -9439.72832
## 692 693 694 695 696 697
## -3385.04134 -2990.93299 6003.87177 -10224.42942 -7801.29693 -8534.24009
## 698 699 700 701 702 703
## -6230.42032 -4713.57871 3098.86799 -4355.11240 -1861.42682 4262.42395
## 704 705 706 707 708 709
## 31180.12189 9780.61900 23753.76796 2121.87339 8746.48810 23369.82225
## 710 711 712 713 714 715
## 7127.73229 -17635.69535 5233.95929 -5025.94546 255.59451 815.33903
## 716 717 718 719 720 721
## -16945.61963 -5069.61785 3495.68903 -2827.04560 -12810.16119 4368.48748
## 722 723 724 725 726 727
## -5426.89010 844.43792 -3816.47391 -12344.69802 1402.18623 -1803.81248
## 728 729 730 731
## -9709.88679 17289.95893 1927.88858 -2553.78659
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17288.68 20115.03 24337.90 24057.93 26394.08 23746.24 24457.32 19724.31
## 10 11 12 13 14 15 16 17
## 19462.79 16824.99 17596.77 14350.09 14400.88 15060.67 16744.84 15077.15
## 18 19 20 21 22 23 24 25
## 16104.25 15482.36 22513.00 21603.71 21087.37 22963.56 22294.48 22942.25
## 26 27 28 29 30 31 32 33
## 24774.70 18747.50 20461.03 28241.03 28298.26 27973.57 25620.32 27012.50
## 34 35 36 37 38 39 40 41
## 30827.42 31173.30 32572.17 30100.98 34102.88 37287.49 34365.13 31199.34
## 42 43 44 45 46 47 48 49
## 30055.55 20703.99 28167.72 30586.38 31667.62 38455.70 37953.98 42582.06
## 50 51 52 53 54 55 56 57
## 46787.34 39538.67 34146.85 29205.97 22400.54 28644.54 25250.19 21574.88
## 58 59 60 61 62 63 64 65
## 25950.99 27201.32 27496.16 27904.97 23806.10 40276.01 42106.48 37387.22
## 66 67 68 69 70 71 72 73
## 41563.38 46443.07 57033.20 55061.50 40412.65 37936.76 40932.58 35274.42
## 74 75 76 77 78 79 80 81
## 30759.94 21531.39 24709.45 20664.97 22735.77 17668.65 19669.33 18901.52
## 82 83 84 85 86 87 88 89
## 17936.36 16019.41 17287.84 20869.49 25254.90 26237.52 26262.30 26874.52
## 90 91 92 93 94 95 96 97
## 30969.42 29808.53 30800.57 28879.17 28084.84 28450.29 28853.90 22478.34
## 98 99 100 101 102 103 104 105
## 25471.32 18552.79 17417.29 15472.35 15769.92 16442.35 20882.25 19972.47
## 106 107 108 109 110 111 112 113
## 23457.87 23249.38 24913.23 27768.94 25285.37 21754.88 22028.28 24655.19
## 114 115 116 117 118 119 120 121
## 35440.18 33646.25 35470.28 38446.65 40388.25 38093.59 32952.51 29320.68
## 122 123 124 125 126 127 128 129
## 31388.26 29680.81 30853.72 38397.84 38042.92 37111.34 33997.82 35765.98
## 130 131 132 133 134 135 136 137
## 41124.35 40568.74 31834.86 33090.39 36256.80 32693.73 31092.81 30184.51
## 138 139 140 141 142 143 144 145
## 26766.74 28170.82 27942.13 25645.37 27654.97 26289.80 19938.77 22947.11
## 146 147 148 149 150 151 152 153
## 20789.72 23745.03 24281.06 25859.81 26040.55 27682.60 28973.81 31983.76
## 154 155 156 157 158 159 160 161
## 27487.11 26755.47 24328.63 30179.55 41527.30 39869.77 37254.01 42237.51
## 162 163 164 165 166 167 168 169
## 43635.76 47026.64 42525.79 37867.35 43252.85 59435.60 61631.52 60057.23
## 170 171 172 173 174 175 176 177
## 56906.19 55317.16 57999.93 57031.18 49379.77 52182.59 55897.79 55933.72
## 178 179 180 181 182 183 184 185
## 63010.32 53582.20 50356.95 41232.96 32834.30 36316.85 46346.29 45806.29
## 186 187 188 189 190 191 192 193
## 51715.43 57418.31 68122.28 73366.43 67107.32 67299.34 74317.84 70025.97
## 194 195 196 197 198 199 200 201
## 65581.60 54904.26 48978.15 50392.01 46018.99 38245.19 44622.85 42863.24
## 202 203 204 205 206 207 208 209
## 42503.86 42978.70 49722.92 58546.99 58179.31 59891.48 61535.14 65298.53
## 210 211 212 213 214 215 216 217
## 74695.68 66836.52 55112.14 49760.46 40822.79 37802.81 40893.65 30986.98
## 218 219 220 221 222 223 224 225
## 47836.09 55103.22 55998.51 78597.07 86037.14 88025.68 95561.39 86578.63
## 226 227 228 229 230 231 232 233
## 80621.05 80206.04 76880.57 76048.21 80750.50 82104.77 76581.03 71829.06
## 234 235 236 237 238 239 240 241
## 77441.26 64186.94 56281.82 48291.47 39964.54 44125.71 46262.67 39761.37
## 242 243 244 245 246 247 248 249
## 33487.28 43694.36 37983.43 41890.27 34210.87 32926.22 36554.70 39357.89
## 250 251 252 253 254 255 256 257
## 30254.59 36149.12 39922.67 45079.35 47777.74 47274.04 57499.45 74868.37
## 258 259 260 261 262 263 264 265
## 74684.77 68044.48 69515.45 65763.95 67199.49 61002.94 50356.20 46412.69
## 266 267 268 269 270 271 272 273
## 46620.56 42755.83 51495.42 47795.82 51912.16 50042.54 54080.08 54373.93
## 274 275 276 277 278 279 280 281
## 60347.24 57998.82 67600.87 61570.20 61778.69 60139.86 65840.63 59617.08
## 282 283 284 285 286 287 288 289
## 56206.13 45835.03 44243.53 61349.44 66838.99 67245.45 64681.97 63776.03
## 290 291 292 293 294 295 296 297
## 67747.66 71629.24 52765.08 42939.67 36994.61 47240.34 50458.41 49578.85
## 298 299 300 301 302 303 304 305
## 73597.84 79499.67 80162.44 84741.09 82952.87 78019.54 81460.96 56543.71
## 306 307 308 309 310 311 312 313
## 52832.22 52514.34 46350.00 43579.89 47156.84 39763.60 38495.06 33096.43
## 314 315 316 317 318 319 320 321
## 36853.26 36040.60 39844.00 37843.19 63440.68 61297.66 62909.35 70848.91
## 322 323 324 325 326 327 328 329
## 73218.76 98513.85 96903.40 72910.55 71717.20 70086.55 62097.19 59240.48
## 330 331 332 333 334 335 336 337
## 29408.85 33069.42 33505.74 35808.69 35154.35 40879.16 41946.89 37229.24
## 338 339 340 341 342 343 344 345
## 36449.40 36575.14 31928.35 37883.46 38542.01 38798.57 39667.97 41440.83
## 346 347 348 349 350 351 352 353
## 43249.50 43002.98 35999.22 26636.02 31941.61 30810.94 30403.99 28037.86
## 354 355 356 357 358 359 360 361
## 32682.54 36409.39 40840.38 39042.61 40293.69 42416.52 49757.73 50304.30
## 362 363 364 365 366 367 368 369
## 50504.49 52949.66 50451.47 49900.07 42599.00 39816.25 36021.15 33816.06
## 370 371 372 373 374 375 376 377
## 29902.64 37137.32 39407.57 47235.51 41251.17 40702.38 39250.36 38786.55
## 378 379 380 381 382 383 384 385
## 29720.91 34285.44 27387.85 35547.18 45804.80 49344.22 47630.19 49607.34
## 386 387 388 389 390 391 392 393
## 55782.09 65198.31 58459.16 52967.75 52698.43 60024.16 60543.71 69149.13
## 394 395 396 397 398 399 400 401
## 58334.29 59889.74 59453.06 58940.69 57438.63 56209.23 43064.42 51587.46
## 402 403 404 405 406 407 408 409
## 50595.17 49568.94 55923.56 48520.41 47836.58 46175.46 41884.20 40723.44
## 410 411 412 413 414 415 416 417
## 38801.42 32938.61 40754.03 43659.05 38370.19 33494.21 48257.53 52074.32
## 418 419 420 421 422 423 424 425
## 55974.97 48498.98 44849.06 43534.68 47095.03 35598.46 35329.64 29630.23
## 426 427 428 429 430 431 432 433
## 35179.37 43446.35 50288.32 47077.31 44167.34 41114.90 41003.90 37507.35
## 434 435 436 437 438 439 440 441
## 33673.84 30928.01 32492.63 34328.56 32347.69 37178.45 43338.82 40114.81
## 442 443 444 445 446 447 448 449
## 39822.94 42807.49 40708.94 44669.43 39950.91 31048.42 29894.34 41168.67
## 450 451 452 453 454 455 456 457
## 40852.52 46462.61 42130.48 42477.60 44086.32 47777.49 37725.38 42539.41
## 458 459 460 461 462 463 464 465
## 37995.94 45509.97 49004.41 51605.92 48359.24 50683.00 50883.07 52617.41
## 466 467 468 469 470 471 472 473
## 52118.67 55041.03 52388.73 57403.20 50712.12 48340.41 46936.83 43585.72
## 474 475 476 477 478 479 480 481
## 47314.55 54723.64 49191.10 50881.96 45708.67 44099.45 46911.81 36402.95
## 482 483 484 485 486 487 488 489
## 30011.29 31867.03 34544.11 36023.25 36982.09 30669.10 43108.28 49720.07
## 490 491 492 493 494 495 496 497
## 56500.72 51250.63 56048.55 63626.75 67410.46 53766.66 44413.00 42452.32
## 498 499 500 501 502 503 504 505
## 42773.18 43558.59 38085.38 40466.88 45729.85 51370.17 52074.69 52184.45
## 506 507 508 509 510 511 512 513
## 45932.15 47262.39 43548.80 46284.22 45952.35 39713.80 40836.66 40017.81
## 514 515 516 517 518 519 520 521
## 41115.40 43736.23 36628.41 31942.10 55658.08 63759.43 67385.81 60813.03
## 522 523 524 525 526 527 528 529
## 62142.78 75629.62 82555.58 57688.00 52595.36 49313.74 53660.86 53170.79
## 530 531 532 533 534 535 536 537
## 43425.78 48381.29 60949.67 55510.41 58883.33 62842.09 59916.13 54983.05
## 538 539 540 541 542 543 544 545
## 48492.90 47158.58 55037.93 54790.17 47404.36 49611.80 49439.73 50128.51
## 546 547 548 549 550 551 552 553
## 40845.58 32740.77 37050.97 45108.47 44906.91 46600.37 40653.81 49601.15
## 554 555 556 557 558 559 560 561
## 50748.04 40601.66 50073.14 57878.65 57248.22 60819.72 56617.79 68292.62
## 562 563 564 565 566 567 568 569
## 84859.65 75022.85 63669.74 68056.75 66194.81 67372.80 59004.24 43110.01
## 570 571 572 573 574 575 576 577
## 50067.56 55929.63 57103.78 59164.17 59806.50 56953.88 69111.23 58562.11
## 578 579 580 581 582 583 584 585
## 52330.87 59876.98 61370.00 54514.41 60735.24 56343.64 53409.76 66880.87
## 586 587 588 589 590 591 592 593
## 52415.73 59706.40 58805.41 52573.43 51883.66 52157.69 42941.28 45717.71
## 594 595 596 597 598 599 600 601
## 40383.38 44598.20 53297.52 46677.42 52493.65 54854.09 60481.77 56667.96
## 602 603 604 605 606 607 608 609
## 61446.56 53076.76 54943.04 55713.70 58005.01 58574.87 58118.98 52338.93
## 610 611 612 613 614 615 616 617
## 59349.00 57423.27 54541.66 51271.81 44286.14 55712.34 59571.37 50573.00
## 618 619 620 621 622 623 624 625
## 60899.01 65053.37 58590.72 80655.86 65887.34 58272.72 60261.02 55648.29
## 626 627 628 629 630 631 632 633
## 46054.30 56683.45 37381.86 37243.11 46741.47 57147.51 55206.41 83725.39
## 634 635 636 637 638 639 640 641
## 73983.36 76168.01 77793.33 72558.23 65330.74 61989.66 49983.07 48363.33
## 642 643 644 645 646 647 648 649
## 47263.01 45756.37 44152.84 46810.10 51393.71 66255.02 80573.72 77718.13
## 650 651 652 653 654 655 656 657
## 78615.28 84443.78 97790.02 92587.00 63074.74 60545.20 57521.88 58499.30
## 658 659 660 661 662 663 664 665
## 54966.33 45449.26 47816.14 52102.31 51300.43 62777.54 62656.14 62943.54
## 666 667 668 669 670 671 672 673
## 51483.53 52833.09 53844.09 49216.99 43240.83 46253.50 43869.90 47331.15
## 674 675 676 677 678 679 680 681
## 45100.04 37990.12 32687.25 32684.77 35413.63 40110.97 42354.83 40373.97
## 682 683 684 685 686 687 688 689
## 40397.38 40842.86 35226.70 41509.71 41010.84 41307.91 43288.77 53918.04
## 690 691 692 693 694 695 696 697
## 62313.36 70303.59 59678.90 55715.93 52621.13 57737.43 48101.44 41846.67
## 698 699 700 701 702 703 704 705
## 35787.13 32530.29 31021.42 36487.68 34764.00 35431.72 41321.16 69770.52
## 706 707 708 709 710 711 712 713
## 75883.95 93302.41 89648.65 92224.89 107139.84 105988.98 83516.90 83861.66
## 714 715 716 717 718 719 720 721
## 75263.55 72387.52 70378.91 53235.33 48667.45 52133.90 49657.02 38852.08
## 722 723 724 725 726 727 728 729
## 44379.18 40677.85 42906.47 40797.27 31572.81 35494.53 36115.17 29797.47
## 730 731
## 47732.40 49963.50
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8276
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.253695 0.502916 3.348322
## t2* 2196.441514 23.654460 255.296494
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.989032 7.38663 13.85153
## 2 lag_depvar 1824.357897 2203.60714 2659.60814Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Wed Jul 03 19:15:07 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Wed Jul 03 19:15:14 2024
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## =-=-=-=-= Iteration 4000 Wed Jul 03 19:15:21 2024
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## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Wed Jul 03 19:15:36 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Wed Jul 03 19:15:43 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Wed Jul 03 19:15:50 2024
## =-=-=-=-=
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## =-=-=-=-= Iteration 16000 Wed Jul 03 19:16:05 2024
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## =-=-=-=-= Iteration 18000 Wed Jul 03 19:16:12 2024
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 5.441667 | 5.195333 | 5.410333 | 5.849167 | 6.352018 |
| Comida | 315.368000 | 366.009167 | 312.386750 | 317.896583 | 338.279054 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 61.806667 | 38.104750 | 47.072333 | 29.523000 | 36.263564 |
| Enceres | 45.813000 | 18.259750 | 24.219750 | 14.801167 | 26.007091 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 9.070236 |
| Gas/Bencina | 45.176833 | 42.636000 | 45.575000 | 13.583667 | 30.903582 |
| Diosi | 49.019833 | 55.804250 | 31.180667 | 52.687833 | 43.920873 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 21.991667 | 12.829167 | 25.156667 | 19.086917 | 19.021600 |
| Netflix | 2.782833 | 8.713833 | 7.151583 | 7.028750 | 7.098073 |
| Otros | 60.666667 | 5.481667 | 5.000000 | 0.000000 | 8.905091 |
| Total | 608.067167 | 563.738000 | 505.988083 | 474.453167 | 525.821182 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2382, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-07-09 00:04:58 sería de: 37.945 pesos// Percentil 95% más alto proyectado: 41.195,98
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37608.45 | 37607.72 |
| Lo.80 | 37614.16 | 37612.93 |
| Point.Forecast | 37945.40 | 38726.12 |
| Hi.80 | 39776.26 | 43514.26 |
| Hi.95 | 40780.94 | 46048.95 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,1)
##
## Coefficients:
## ma1
## -0.8126
## s.e. 0.0917
##
## sigma^2 = 30845: log likelihood = -421.62
## AIC=847.25 AICc=847.44 BIC=851.56paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.2072 443.3574 18.5028
## s.e. 0.1219 223.9855 7.0332
##
## sigma^2 = 27735: log likelihood = -423.21
## AIC=854.41 AICc=855.08 BIC=863.11forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 864.6484 | 718.9666 | 775.412 |
| Lo.80 | 980.0311 | 838.1142 | 865.267 |
| Point.Forecast | 1197.9942 | 1063.1893 | 1064.393 |
| Hi.80 | 1415.9573 | 1328.2112 | 1309.344 |
| Hi.95 | 1531.3401 | 1468.5054 | 1461.072 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))